Overview

In a challenge lab you’re given a scenario and a set of tasks. Instead of following step-by-step instructions, you will use the skills learned from the labs in the course to figure out how to complete the tasks on your own! An automated scoring system (shown on this page) will provide feedback on whether you have completed your tasks correctly.

When you take a challenge lab, you will not be taught new Google Cloud concepts. You are expected to extend your learned skills, like changing default values and reading and researching error messages to fix your own mistakes.

To score 100% you must successfully complete all tasks within the time period!

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

Note: Use an Incognito (recommended) or private browser window to run this lab. This prevents conflicts between your personal account and the student account, which may cause extra charges incurred to your personal account.

• Time to complete the lab—remember, once you start, you cannot pause a lab.

Note: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that account.

Challenge scenario

You are starting your career as a junior cloud architect. In this role, you have been assigned to work on a team project that requires you to use the Cloud Speech API services in Google Cloud.

You are expected to have the skills and knowledge to complete the tasks that follow.

Your challenge

For this challenge, you are required to transcribe speech to text in different languages using the Cloud Speech API.

You need to:

• Create synthetic speech from text using the Text-to-Speech API.

• Create an API key.

• Perform speech to text transcription with the Cloud Speech API.

• Translate text with the Cloud Translation API.

• Detect a language with the Cloud Translation API.

For this challenge lab, a virtual machine (VM) instance named lab-vm has been configured for you to complete tasks 2 through 5.

Each task is described in detail below, good luck!

Task 1. Create an API key

1. For this task, you need to create an API key to use in this and other tasks when sending a request to the Speech-to-Text API.

Click here for hint!

2. Save the API key to use in other tasks.

Click Check my progress to verify the objective.

Create an API key

Check my progress

Task 2. Create synthetic speech from text using the Text-to-Speech API

1. For this task, connect to the VM instance lab-vm provisioned for you via SSH.

2. Activate the virtual environment using the source venv/bin/activate command.

3. Using a text editor (such as nano or vim), create a file named synthesize-text.json and paste the following into the file:

{
    'input':{
        'text':'Cloud Text-to-Speech API allows developers to include
           natural-sounding, synthetic human speech as playable audio in
           their applications. The Text-to-Speech API converts text or
           Speech Synthesis Markup Language (SSML) input into audio data
           like MP3 or LINEAR16 (the encoding used in WAV files).'
    },
    'voice':{
        'languageCode':'en-gb',
        'name':'en-GB-Standard-A',
        'ssmlGender':'FEMALE'
    },
    'audioConfig':{
        'audioEncoding':'MP3'
    }
}

4. Call the Text-to-Speech API to synthesize the text of the synthesize-text.json file, and store the result in a file named synthesize-text.txt.

5. Using a text editor (such as nano or vim), create a file named tts_decode.py and paste the following code into that file:

import argparse
from base64 import decodebytes
import json

"""
Usage:
        python tts_decode.py --input "synthesize-text.txt" \
        --output "synthesize-text-audio.mp3"

"""

def decode_tts_output(input_file, output_file):
    """ Decode output from Cloud Text-to-Speech.

    input_file: the response from Cloud Text-to-Speech
    output_file: the name of the audio file to create

    """

    with open(input_file) as input:
        response = json.load(input)
        audio_data = response['audioContent']

        with open(output_file, "wb") as new_file:
            new_file.write(decodebytes(audio_data.encode('utf-8')))

if __name__ == '__main__':
    parser = argparse.ArgumentParser(
        description="Decode output from Cloud Text-to-Speech",
        formatter_class=argparse.RawDescriptionHelpFormatter)
    parser.add_argument('--input',
                       help='The response from the Text-to-Speech API.',
                       required=True)
    parser.add_argument('--output',
                       help='The name of the audio file to create',
                       required=True)

    args = parser.parse_args()
    decode_tts_output(args.input, args.output)

6. Now, to create an audio file using the response you received from the Text-to-Speech API, run the following command from Cloud Shell:

python tts_decode.py --input "synthesize-text.txt" --output "synthesize-text-audio.mp3"

This creates a new MP3 file named synthesize-text-audio.mp3.

7. Finally, download the audio file via the DOWNLOAD FILE option of the VM instance's SSH session in order to listen to it.

Click Check my progress to verify the objective.

Create synthetic speech from text using the Text-to-Speech API

Check my progress

Task 3. Perform speech to text transcription with the Cloud Speech API

Note: This lab uses a pre-recorded file that's available on Cloud Storage: gs://cloud-samples-data/speech/corbeau_renard.flac. You can listen to this file.

Click here for hint!

1. For this task, connect to the VM instance lab-vm provisioned for you via SSH.

2. Using a text editor (such as nano or vim), create a file named speech_request.json as your API request to transcribe the audio file available at the gs://cloud-samples-data/speech/corbeau_renard.flac location to French.

3. Call speech_request.json and store the result in a file named speech_response_fr.json.

Click Check my progress to verify the objective.

Create the API request for transcription in French language

Check my progress

Task 4. Translate text with the Cloud Translation API

1. For this task, connect to the VM instance lab-vm provisioned for you via SSH.

2. Translate the これは日本語です。 sentence to the English language by calling the Cloud Translation API and store the result in the translation_response.txt file.

Click Check my progress to verify the objective.

Translate text with the Cloud Translation API

Check my progress

Task 5. Detect a language with the Cloud Translation API

1. For this task, connect to the VM instance lab-vm provisioned for you via SSH.

2. Detect the language of the Este%é%japonês. sentence by calling the Cloud Translation API and store the result in the detected_response.txt file.

Click Check my progress to verify the objective.

Detect a language with the Cloud Translation API

---

Solution of Lab

# Get the zone of your lab VM
export ZONE=$(gcloud compute instances list lab-vm --format 'csv[no-heading](zone)')

# SSH into the lab VM
gcloud compute ssh lab-vm --project=$DEVSHELL_PROJECT_ID --zone=$ZONE --quiet

# Download the setup script
curl -LO raw.githubusercontent.com/ArcadeCrew/Google-Cloud-Labs/refs/heads/main/Cloud%20Speech%20API%203%20Ways%20Challenge%20Lab/arcadecrew.sh

# Make the script executable
sudo chmod +x arcadecrew.sh

# Run the script to set up your environment
./arcadecrew.sh